{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T17:02:56Z","timestamp":1789837376094,"version":"4.0.1"},"reference-count":58,"publisher":"IOP Publishing","issue":"11","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61971118"],"award-info":[{"award-number":["61971118"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Plan of Liaoning Province","award":["2021JH1\/10400051"],"award-info":[{"award-number":["2021JH1\/10400051"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["N2216014"],"award-info":[{"award-number":["N2216014"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D + 3D framework for multi-scale airway tree segmentation.\n                    <jats:italic>Approach<\/jats:italic>\n                    . In stage 1, we use a 2D full airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale atros spatial pyramid and Atros Residual Skip connection modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 as\n                    <jats:italic>a priori<\/jats:italic>\n                    information. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added false positive losses and false negative losses to improve the segmentation performance of airway branches within the lungs.\n                    <jats:italic>Main results<\/jats:italic>\n                    . We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest dice similarity coefficient (DSC) of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other state-of-the-art methods to increase detected tree length rate by up to 46.33% and detected tree branch rate by up to 42.97%.\n                    <jats:italic>Significance<\/jats:italic>\n                    . The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad4300","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T18:46:51Z","timestamp":1713984411000},"page":"115027","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["An end-to-end multi-scale airway segmentation framework based on pulmonary CT image"],"prefix":"10.1088","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6750-1745","authenticated-orcid":true,"given":"Ye","family":"Yuan","sequence":"first","affiliation":[{"name":"Northeastern University"},{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Tan","sequence":"additional","affiliation":[{"name":"Northeastern University"},{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8360-3605","authenticated-orcid":true,"given":"Lisheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Bao","sequence":"additional","affiliation":[{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Zhu","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Nanjing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Affiliated Zhongshan Hospital of Dalian University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Affiliated Zhongshan Hospital of Dalian University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"key":"pmbad4300bib1","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1109\/TMI.2003.815905","type":"journal-article","article-title":"Segmentation and analysis of the human airway tree from three-dimensional x-ray CT images","volume":"22","author":"Aykac","year":"2003","journal-title":"IEEE Trans. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2023-09-11","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-04-24","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-05-21","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}